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Радиопромышленность. 2019; 29: 79-90

Разработка проектно-ориентированной инфраструктуры пространственных данных с применением облачных технологий

Ямашкин С. А., Ямашкин А. А., Федосин С. А.

https://doi.org/10.21778/2413-9599-2019-29-3-79-90

Аннотация

Список литературы

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31. Yamashkin S. A., Radovanovi M. M., Yamashkin A. A., Barmin N. N., Zanozin V. V., Petrović M. D. Problems of Designing Geoportal Interfaces. GeoJournal of Tourism and Geosites, 2019, vol. 24, iss. 1, pp. 88–101. DOI: 10.30892/gtg.24108-345.

Radio industry (Russia). 2019; 29: 79-90

Development of project-oriented spatial data infrastructure using cloud technologies

Yamashkin S. A., Yamashkin A. A., Fedosin S. A.

https://doi.org/10.21778/2413-9599-2019-29-3-79-90

Abstract

The article includes the issues of design, development and introduction of project-oriented spatial data infrastructures (SDIs) that build the information space to solve pressing challenges in economy, ecology, social services, in the field of preparation of pre-investment, urban planning, pre-project, project documentation, and natural disaster forecasting.

It also provides an overview of a historical development of spatial data infrastructures in Russia and in the world. Based on an analysis of a historical landscape within the challenging area, authors have identified the following system components of SDIs: users and professionals, data, technologies, standards, regulatory frameworks, and institutional procedures. There is a proposed platform solution architecture to build SDI, summarized in a form of a structure-component scheme. It rests upon the hypothesis that in order to optimize spatial data storage and application-related processes, the project-oriented SDI needs to include loosely bound and closely bound subsystems for spatial data storage (cloud or local storages), analysis and synthesis modules, as well as modules for visualization and distribution of spatial data (as geoportal systems).

References

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6. Masser I. Changing Notions of a Spatial Data Infrastructure. In: SDI Convergence: Research, Emerging Trends, and Critical Assessment. Netherlands Geodetic Commission, 2009, pp. 219–228.

7. Executive Order of the White House. Coordinating Geographic Data Acquisition and Access: The National Spatial Data Infrastructure. Washington, DC: Office of the Press Secretary, The White House, 1994 [Elektronnyi resurs]. URL: https://fas.org/irp/offdocs/eo12906.htm (data obrashcheniya: 16.07.2019).

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21. Dai W., Qiu L., Wu A. Qiu, M. Cloud Infrastructure Resource Allocation for Big Data Applications. IEEE Transactions on Big Data, 2018, vol. 4, pp. 313–324.

22. Dahiya D., Mathew S. K. IT assets, IT infrastructure performance and IT capability: a framework for e-government. Transforming Government: People, Process and Policy, 2016, vol. 10, no. 3, pp. 411–433. DOI: 10.1108/TG-07-2015-0031.

23. Eldawy A., Mokbel M. F., Alharthi S., Alzaidy A., Tarek K., Ghani S. A mapreduce-based system for querying and visualizing spatio-temporal satellite data. In 2015 IEEE 31st International Conference on Data Engineering (ICDE), 2015, pp. 1585–1596. DOI: 10.1109/ICDE.2015.7113427.

24. Zhao J., Ishikawa Y., Xiao C., Sugiura K. Histogram Construction for Difference Analysis of Spatio-Temporal Data on Array DBMS. Lecture Notes in Computer Science on pages, 2018, pp. 41–52. DOI: 10.1007/978-3-319-92013-9_4.

25. Yue S., Chen M., Wen Y., Lu G. Service-oriented model-encapsulation strategy for sharing and integrating heterogeneous geo-analysis models in an open web environment. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, vol. 114, pp. 258–273.

26. Bengio Y., Courville A., Vincent P. Representation learning: A review and new perspectives. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 2013, vol. 35, iss. 8, pp. 1798–1828. DOI: 10.1109/TPAMI.2013.50.

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